A Semi-Automated Framework for Standardized Vertebral Measurement with Enhanced Reproducibility in Lumbar Spine MRI Analysis

A semi-automated framework for vertebral measurement has been developed to overcome clinical limitations of subjectivity and poor reproducibility in spinal assessment. The framework integrates watershed segmentation with level-set functions and deterministic cylindrical modeling to convert pixel-based measurements to physical dimensions, achieving 2% reproducibility error. Interactive region-of-interest selection enables the effective handling of multi-vertebrae cases while preserving clinical expertise input. Validation using a lumbar spine MRI dataset on 515 patients confirms measurements fall within established anatomical parameters for L3–L5 vertebrae. This methodology provides a transparent, reproducible approach for standardized vertebral assessment that balances automation with clinical reasoning, offering immediate implementation potential without the computational demands and regulatory challenges associated with complex AI systems.